Intelligent Price Forecasting System for Spice Traders with Machine Learning

Kiran Basavannappagowda (), J. B. Simha (), M. P. Praveen () and Gundlupet Sadananda Murthy ()
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Kiran Basavannappagowda: REVA Academy for Corporate Excellence -RACE, REVA University Rukmini Knowledge Park
J. B. Simha: REVA Academy for Corporate Excellence -RACE, REVA University Rukmini Knowledge Park
M. P. Praveen: Numentrix Consulting LLP, Director – Operations
Gundlupet Sadananda Murthy: Director- samparkbindhu

A chapter in Proceedings of the International Conference on Policies, Processes and Practices for Transforming Underdeveloped Economies into Developed Economies (PPP-UD 2025), 2025, pp 376-386 from Springer

Abstract: Abstract Spices are essential agricultural products that hold considerable economic importance and fulfill various roles in culinary, medicinal, and industrial fields. Black pepper, a spice traded worldwide, experiences frequent price changes due to seasonal variations, inconsistent quality, and disruptions in the supply chain. This research introduces a forecasting model aimed at predicting black pepper prices in local markets of Karnataka. Conventional techniques such as Simple Moving Average (SMA), Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) often yield subpar results when faced with irregular data conditions. To overcome this challenge, a Multiple Linear Regression (MLR) model with lag features was developed, utilizing domain-specific feature engineering. The data processing pipeline included steps for managing missing values, outliers, normalization, and identifying temporal patterns. A knowledge-driven nearest neighbor analysis was employed to improve forecasting accuracy. Among all the models assessed, the MLR model recorded the lowest Mean Absolute Percentage Error (MAPE) of 0.22%. The proposed work also features a user interface designed to aid traders in making informed decisions and allows for a more in-depth analysis of black pepper trading trends.

Keywords: Black Pepper; Commodity Price Forecasting; Deep Learning; Time Series Analysis; MLR; Lag feature; SMA; ARIMA; LSTM; GRU; Machine Learning; Price Volatility; Market Prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-894-3_27

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DOI: 10.2991/978-94-6463-894-3_27

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